deepchem skill by davila7

DeepChem

Overview

DeepChem is a comprehensive Python library for applying machine learning to chemistry, materials science, and biology. Enable molecular property prediction, drug discovery, materials design, and biomolecule analysis through specialized neural networks, molecular featurization methods, and pretrained models.

When to Use This Skill

This skill should be used when:

Loading and processing molecular data (SMILES strings, SDF files, protein sequences)
Predicting molecular properties (solubility, toxicity, binding affinity, ADMET properties)
Training models on chemical/biological datasets
Using MoleculeNet benchmark datasets (Tox21, BBBP, Delaney, etc.)
Converting molecules to ML-ready features (fingerprints, graph representations, descriptors)
Implementing graph neural networks for molecules (GCN, GAT, MPNN, AttentiveFP)
Applying transfer learning with pretrained models (ChemBERTa, GROVER, MolFormer)
Predicting crystal/materials properties (bandgap, formation energy)
Analyzing protein or DNA sequences

Core Capabilities

1. Molecular Data Loading and Processing

DeepChem provides specialized loaders for various chemical data formats:

import deepchem as dc
 
# Load CSV with SMILES
featurizer = dc.feat.CircularFingerprint(radius=2

Key Loaders:

CSVLoader: Tabular data with molecular identifiers
SDFLoader: Molecular structure files
FASTALoader: Protein/DNA sequences
ImageLoader: Molecular images
JsonLoader: JSON-formatted datasets

2. Molecular Featurization

Convert molecules into numerical representations for ML models.

Decision Tree for Featurizer Selection

Example Featurization

Selection Guide:

Small datasets (<1K): CircularFingerprint or RDKitDescriptors
Medium datasets (1K-100K): CircularFingerprint or graph featurizers
Large datasets (>100K): Graph featurizers (MolGraphConvFeaturizer, DMPNNFeaturizer)
Transfer learning: Pretrained model featurizers (GroverFeaturizer)

See references/api_reference.md for complete featurizer documentation.

3. Data Splitting

Critical: For drug discovery tasks, use ScaffoldSplitter to prevent data leakage from similar molecular structures appearing in both training and test sets.

Available Splitters:

ScaffoldSplitter: Split by molecular scaffolds (prevents leakage)
ButinaSplitter: Clustering-based molecular splitting
MaxMinSplitter: Maximize diversity between sets
RandomSplitter: Random splitting
RandomStratifiedSplitter: Preserves class distributions

4. Model Selection and Training

Quick Model Selection Guide

Dataset Size	Task	Recommended Model	Featurizer
< 1K samples	Any	SklearnModel (RandomForest)	CircularFingerprint
1K-100K	Classification/Regression	GBDTModel or MultitaskRegressor	CircularFingerprint
> 100K	Molecular properties	GCNModel, AttentiveFPModel, DMPNNModel	MolGraphConvFeaturizer
Any (small preferred)	Transfer learning	ChemBERTa, GROVER, MolFormer	Model-specific
Crystal structures	Materials properties	CGCNNModel, MEGNetModel	Structure-based
Protein sequences	Protein properties	ProtBERT	Sequence-based

Example: Traditional ML

Example: Deep Learning

Example: Graph Neural Networks

5. MoleculeNet Benchmarks

Quick access to 30+ curated benchmark datasets with standardized train/valid/test splits:

Common Datasets:

Classification: load_tox21(), load_bbbp(), load_hiv(), load_clintox()
Regression: load_delaney(), load_freesolv(), load_lipo()
Quantum properties: load_qm7(), load_qm8(), load_qm9()
Materials: load_perovskite(), load_bandgap(), load_mp_formation_energy()

See references/api_reference.md for complete dataset list.

6. Transfer Learning

Leverage pretrained models for improved performance, especially on small datasets:

When to use transfer learning:

Small datasets (< 1000 samples)
Novel molecular scaffolds
Limited computational resources
Need for rapid prototyping

Use the scripts/transfer_learning.py script for guided transfer learning workflows.

7. Model Evaluation

8. Making Predictions

Typical Workflows

Workflow A: Quick Benchmark Evaluation

For evaluating a model on standard benchmarks:

Workflow B: Custom Data Prediction

For training on custom molecular datasets:

Workflow C: Transfer Learning on Small Dataset

For leveraging pretrained models:

See references/workflows.md for 8 detailed workflow examples covering molecular generation, materials science, protein analysis, and more.

Example Scripts

This skill includes three production-ready scripts in the scripts/ directory:

1. `predict_solubility.py`

Train and evaluate solubility prediction models. Works with Delaney benchmark or custom CSV data.

2. `graph_neural_network.py`

Train various graph neural network architectures on molecular data.

3. `transfer_learning.py`

Fine-tune pretrained models (ChemBERTa, GROVER) on molecular property prediction tasks.

Common Patterns and Best Practices

Pattern 1: Always Use Scaffold Splitting for Molecules

Pattern 2: Normalize Features and Targets

Pattern 3: Start Simple, Then Scale

Start with Random Forest + CircularFingerprint (fast baseline)
Try XGBoost/LightGBM if RF works well
Move to deep learning (MultitaskRegressor) if you have >5K samples
Try GNNs if you have >10K samples
Use transfer learning for small datasets or novel scaffolds

Pattern 4: Handle Imbalanced Data

Pattern 5: Avoid Memory Issues

Common Pitfalls

Issue 1: Data Leakage in Drug Discovery

Problem: Using random splitting allows similar molecules in train/test sets. Solution: Always use ScaffoldSplitter for molecular datasets.

Issue 2: GNN Underperforming vs Fingerprints

Problem: Graph neural networks perform worse than simple fingerprints. Solutions:

Ensure dataset is large enough (>10K samples typically)
Increase training epochs (50-100)
Try different architectures (AttentiveFP, DMPNN instead of GCN)
Use pretrained models (GROVER)

Issue 3: Overfitting on Small Datasets

Problem: Model memorizes training data. Solutions:

Use stronger regularization (increase dropout to 0.5)
Use simpler models (Random Forest instead of deep learning)
Apply transfer learning (ChemBERTa, GROVER)
Collect more data

Issue 4: Import Errors

Problem: Module not found errors. Solution: Ensure DeepChem is installed with required dependencies:

Reference Documentation

This skill includes comprehensive reference documentation:

`references/api_reference.md`

Complete API documentation including:

All data loaders and their use cases
Dataset classes and when to use each
Complete featurizer catalog with selection guide
Model catalog organized by category (50+ models)
MoleculeNet dataset descriptions
Metrics and evaluation functions
Common code patterns

When to reference: Search this file when you need specific API details, parameter names, or want to explore available options.

`references/workflows.md`

Eight detailed end-to-end workflows:

Molecular property prediction from SMILES
Using MoleculeNet benchmarks
Hyperparameter optimization
Transfer learning with pretrained models
Molecular generation with GANs
Materials property prediction
Protein sequence analysis
Custom model integration

When to reference: Use these workflows as templates for implementing complete solutions.

Installation Notes

Basic installation:

For PyTorch models (GCN, GAT, etc.):

For all features:

If import errors occur, the user may need specific dependencies. Check the DeepChem documentation for detailed installation instructions.

Additional Resources

Official documentation: https://deepchem.readthedocs.io/
GitHub repository: https://github.com/deepchem/deepchem
Tutorials: https://deepchem.readthedocs.io/en/latest/get_started/tutorials.html
Paper: "MoleculeNet: A Benchmark for Molecular Machine Learning"

DeepChem

Overview

When to Use This Skill

This skill should be used when:

Loading and processing molecular data (SMILES strings, SDF files, protein sequences)
Predicting molecular properties (solubility, toxicity, binding affinity, ADMET properties)
Training models on chemical/biological datasets
Using MoleculeNet benchmark datasets (Tox21, BBBP, Delaney, etc.)
Converting molecules to ML-ready features (fingerprints, graph representations, descriptors)
Implementing graph neural networks for molecules (GCN, GAT, MPNN, AttentiveFP)
Applying transfer learning with pretrained models (ChemBERTa, GROVER, MolFormer)
Predicting crystal/materials properties (bandgap, formation energy)
Analyzing protein or DNA sequences

Core Capabilities

1. Molecular Data Loading and Processing

DeepChem provides specialized loaders for various chemical data formats:

import deepchem as dc
 
# Load CSV with SMILES
featurizer = dc.feat.CircularFingerprint(radius=2

Key Loaders:

CSVLoader: Tabular data with molecular identifiers
SDFLoader: Molecular structure files
FASTALoader: Protein/DNA sequences
ImageLoader: Molecular images
JsonLoader: JSON-formatted datasets

2. Molecular Featurization

Convert molecules into numerical representations for ML models.

Decision Tree for Featurizer Selection

Example Featurization

Selection Guide:

Small datasets (<1K): CircularFingerprint or RDKitDescriptors
Medium datasets (1K-100K): CircularFingerprint or graph featurizers
Large datasets (>100K): Graph featurizers (MolGraphConvFeaturizer, DMPNNFeaturizer)
Transfer learning: Pretrained model featurizers (GroverFeaturizer)

See references/api_reference.md for complete featurizer documentation.

3. Data Splitting

Critical: For drug discovery tasks, use ScaffoldSplitter to prevent data leakage from similar molecular structures appearing in both training and test sets.

Available Splitters:

ScaffoldSplitter: Split by molecular scaffolds (prevents leakage)
ButinaSplitter: Clustering-based molecular splitting
MaxMinSplitter: Maximize diversity between sets
RandomSplitter: Random splitting
RandomStratifiedSplitter: Preserves class distributions

4. Model Selection and Training

Quick Model Selection Guide

Dataset Size	Task	Recommended Model	Featurizer
< 1K samples	Any	SklearnModel (RandomForest)	CircularFingerprint
1K-100K	Classification/Regression	GBDTModel or MultitaskRegressor	CircularFingerprint
> 100K	Molecular properties	GCNModel, AttentiveFPModel, DMPNNModel	MolGraphConvFeaturizer
Any (small preferred)	Transfer learning	ChemBERTa, GROVER, MolFormer	Model-specific
Crystal structures	Materials properties	CGCNNModel, MEGNetModel	Structure-based
Protein sequences	Protein properties	ProtBERT	Sequence-based

Example: Traditional ML

Example: Deep Learning

Example: Graph Neural Networks

5. MoleculeNet Benchmarks

Quick access to 30+ curated benchmark datasets with standardized train/valid/test splits:

Common Datasets:

Classification: load_tox21(), load_bbbp(), load_hiv(), load_clintox()
Regression: load_delaney(), load_freesolv(), load_lipo()
Quantum properties: load_qm7(), load_qm8(), load_qm9()
Materials: load_perovskite(), load_bandgap(), load_mp_formation_energy()

See references/api_reference.md for complete dataset list.

6. Transfer Learning

Leverage pretrained models for improved performance, especially on small datasets:

When to use transfer learning:

Small datasets (< 1000 samples)
Novel molecular scaffolds
Limited computational resources
Need for rapid prototyping

Use the scripts/transfer_learning.py script for guided transfer learning workflows.

7. Model Evaluation

8. Making Predictions

Typical Workflows

Workflow A: Quick Benchmark Evaluation

For evaluating a model on standard benchmarks:

Workflow B: Custom Data Prediction

For training on custom molecular datasets:

Workflow C: Transfer Learning on Small Dataset

For leveraging pretrained models:

See references/workflows.md for 8 detailed workflow examples covering molecular generation, materials science, protein analysis, and more.

Example Scripts

This skill includes three production-ready scripts in the scripts/ directory:

1. `predict_solubility.py`

Train and evaluate solubility prediction models. Works with Delaney benchmark or custom CSV data.

2. `graph_neural_network.py`

Train various graph neural network architectures on molecular data.

3. `transfer_learning.py`

Fine-tune pretrained models (ChemBERTa, GROVER) on molecular property prediction tasks.

Common Patterns and Best Practices

Pattern 1: Always Use Scaffold Splitting for Molecules

Pattern 2: Normalize Features and Targets

Pattern 3: Start Simple, Then Scale

Start with Random Forest + CircularFingerprint (fast baseline)
Try XGBoost/LightGBM if RF works well
Move to deep learning (MultitaskRegressor) if you have >5K samples
Try GNNs if you have >10K samples
Use transfer learning for small datasets or novel scaffolds

Pattern 4: Handle Imbalanced Data

Pattern 5: Avoid Memory Issues

Common Pitfalls

Issue 1: Data Leakage in Drug Discovery

Problem: Using random splitting allows similar molecules in train/test sets. Solution: Always use ScaffoldSplitter for molecular datasets.

Issue 2: GNN Underperforming vs Fingerprints

Problem: Graph neural networks perform worse than simple fingerprints. Solutions:

Ensure dataset is large enough (>10K samples typically)
Increase training epochs (50-100)
Try different architectures (AttentiveFP, DMPNN instead of GCN)
Use pretrained models (GROVER)

Issue 3: Overfitting on Small Datasets

Problem: Model memorizes training data. Solutions:

Use stronger regularization (increase dropout to 0.5)
Use simpler models (Random Forest instead of deep learning)
Apply transfer learning (ChemBERTa, GROVER)
Collect more data

Issue 4: Import Errors

Problem: Module not found errors. Solution: Ensure DeepChem is installed with required dependencies:

Reference Documentation

This skill includes comprehensive reference documentation:

`references/api_reference.md`

Complete API documentation including:

All data loaders and their use cases
Dataset classes and when to use each
Complete featurizer catalog with selection guide
Model catalog organized by category (50+ models)
MoleculeNet dataset descriptions
Metrics and evaluation functions
Common code patterns

When to reference: Search this file when you need specific API details, parameter names, or want to explore available options.

`references/workflows.md`

Eight detailed end-to-end workflows:

Molecular property prediction from SMILES
Using MoleculeNet benchmarks
Hyperparameter optimization
Transfer learning with pretrained models
Molecular generation with GANs
Materials property prediction
Protein sequence analysis
Custom model integration

When to reference: Use these workflows as templates for implementing complete solutions.

Installation Notes

Basic installation:

For PyTorch models (GCN, GAT, etc.):

For all features:

If import errors occur, the user may need specific dependencies. Check the DeepChem documentation for detailed installation instructions.

Additional Resources

Official documentation: https://deepchem.readthedocs.io/
GitHub repository: https://github.com/deepchem/deepchem
Tutorials: https://deepchem.readthedocs.io/en/latest/get_started/tutorials.html
Paper: "MoleculeNet: A Benchmark for Molecular Machine Learning"

deepchem

Documentation

Documentation

DeepChem

Overview

When to Use This Skill

Core Capabilities

1. Molecular Data Loading and Processing

2. Molecular Featurization

Decision Tree for Featurizer Selection

Example Featurization

3. Data Splitting

4. Model Selection and Training

Quick Model Selection Guide

Example: Traditional ML

Example: Deep Learning

Example: Graph Neural Networks

5. MoleculeNet Benchmarks

6. Transfer Learning

7. Model Evaluation

8. Making Predictions

Typical Workflows

Workflow A: Quick Benchmark Evaluation

Workflow B: Custom Data Prediction

Workflow C: Transfer Learning on Small Dataset

Example Scripts

1. predict_solubility.py

2. graph_neural_network.py

3. transfer_learning.py

Common Patterns and Best Practices

Pattern 1: Always Use Scaffold Splitting for Molecules

Pattern 2: Normalize Features and Targets

Pattern 3: Start Simple, Then Scale

Pattern 4: Handle Imbalanced Data

Pattern 5: Avoid Memory Issues

Common Pitfalls

Issue 1: Data Leakage in Drug Discovery

Issue 2: GNN Underperforming vs Fingerprints

Issue 3: Overfitting on Small Datasets

Issue 4: Import Errors

Reference Documentation

references/api_reference.md

references/workflows.md

Installation Notes

Additional Resources

Documentation

deepchem

Documentation

DeepChem

Overview

When to Use This Skill

Core Capabilities

1. Molecular Data Loading and Processing

2. Molecular Featurization

Decision Tree for Featurizer Selection

Example Featurization

3. Data Splitting

4. Model Selection and Training

Quick Model Selection Guide

Example: Traditional ML

Example: Deep Learning

Example: Graph Neural Networks

5. MoleculeNet Benchmarks

6. Transfer Learning

7. Model Evaluation

8. Making Predictions

Typical Workflows

Workflow A: Quick Benchmark Evaluation

Workflow B: Custom Data Prediction

Workflow C: Transfer Learning on Small Dataset

Example Scripts

1. predict_solubility.py

2. graph_neural_network.py

3. transfer_learning.py

Common Patterns and Best Practices

Pattern 1: Always Use Scaffold Splitting for Molecules

Pattern 2: Normalize Features and Targets

Pattern 3: Start Simple, Then Scale

Pattern 4: Handle Imbalanced Data

Pattern 5: Avoid Memory Issues

1. `predict_solubility.py`

2. `graph_neural_network.py`

3. `transfer_learning.py`

`references/api_reference.md`

`references/workflows.md`

1. `predict_solubility.py`

2. `graph_neural_network.py`

3. `transfer_learning.py`

`references/api_reference.md`

`references/workflows.md`